Building a large image dataset with high quality annotations for semantic segmentation is costly and time consuming. In order to tackle this problem, semi-supervised approaches have attracted more interests in recent years. Instead of pixel-level annotations, those methods make use of a small set of strong annotations and a large set of weak annotations, such as scribbles [1]} [2]}, bounding boxes [3]} or image-level class labels [4]} [5]}, to reduce the data annotation requirements. Among these three types of weak annotations, image-level class labels are the easiest one to obtain and have been widely used to bridge the gap between image-level and pixel-level annotations. In this paper, we address semi-supervised semantic segmentation using a small set of pixel-level strong annotations and a large set of image-level weak annotations.
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